AI AgentNCT06791499
AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data
An observational study of AI Agent, run by The Eye Hospital of Wenzhou Medical University at 6 sites in China.
Trial summary
The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- AI Agent
- How many people
- 2,000,000 participants
- Where
- 6 sitesChina
- Study dates
- 1 Jul 2023to Jul 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The participants for this study will be selected from multiple healthcare centers and hospitals that maintain comprehensive electronic health records (EHR) and multimodal imaging data. The study population will include patients who have a variety of diseases or health conditions, with data available for diagnosis and disease progression. Participants will have confirmed diagnoses based on clinical records or imaging data, including but not limited to conditions captured by X-rays, CT scans, MRIs, and ultrasounds. Both those with complex health conditions and those with more common illnesses will be included to evaluate the AI system's diagnostic and predictive capabilities across a broad spectrum of cases.Participants from these centers will provide historical health data, and there will be no active intervention beyond the use of their existing clinical and imaging data for training and testing the AI system.
Who may be able to join
- Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.
- Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.
- Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.
- Patients must provide consent for the use of their historical health data for research purposes.
Who cannot join
- Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.
- Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).
Trial locations and contacts
6 sites in 1 country. Contact the study team or a site near you to ask about taking part.
6 sites in 1 country
China6 sites, 6 recruiting
- Recruiting
West China Hospital
Chengdu, Sichuan
Kai Wang, +86 028-85422114, wkai@stu.pku.edu.cn
- Recruiting
Nanfang Hospital
Guangzhou, Guangdong
Zhuomin Li, +86-0577-85397527, chetneyli.1001@gmail.com
- Recruiting
Sun Yat-Sen Memorial Hospital
Guangzhou, Guangdong
Yunfang Yu, +86 020-81332199, yuyf9@mail.sysu.edu.cn
- Recruiting
Sun Yat-sen University Cancer Hospital
Guangzhou, Guangdong
Yuxing Lu, +86 13161233730, yxlu0613@gmail.com
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Sian Liu, +86-0577-88002888, liusan@mail3.sysu.edu.cn
About this study
Official title: AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data
The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.
Read the detailed description
This multi-center, retrospective clinical study is designed to evaluate the application and effectiveness of an AI agent in the medical decision-making process. The AI agent integrates and analyzes multimodal data, including electronic health records (EHR) and various imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds) to predict and diagnose a range of diseases. By leveraging the power of machine learning and data fusion techniques, the AI agent can identify patterns in large and complex datasets, offering insights that may not be immediately apparent through traditional diagnostic methods.The study will compare the AI agent's diagnostic accuracy and disease prediction capabilities with traditional diagnostic practices to assess its potential benefits in clinical settings. Key questions include whether the AI agent can assist in early diagnosis, predict disease progression, and support healthcare professionals in making personalized treatment decisions. Participants will not be required to undergo any additional interventions; they will only provide historical health data, including EHR and relevant imaging data, which will be analyzed by the AI agent. The AI system will then use this data to assist healthcare providers by offering predictions and diagnostic suggestions based on the analysis of the multimodal information. The ultimate goal is to determine whether this AI-driven approach can improve diagnostic accuracy, optimize treatment strategies, and enhance patient outcomes in clinical practice.
- Conditions
- AI Agent
- Keywords
- AI agent
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Disease Prediction Using EHR and Imaging Data | This cohort consists of patients whose historical health data, including electronic health records (EHR) and multimodal imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds), will be analyzed by an AI agent. The AI system will assist in diagnosing and predicting diseases by processing and integrating these diverse data sources. The primary focus is to evaluate the ability of the AI agent to identify patterns and predict disease progression with high accuracy. Participants will not be required to take any additional actions beyond providing their medical history and imaging data. The aim is to assess how well the AI system can support clinical decision-making and improve diagnostic outcomes based on the provided data. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve (AUC)
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Measured: 1 year
F1 Score
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
Measured: 1 year
Secondary outcomes (2)
Sensitivity (True Positive Rate)
Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.
Measured: 1 year
Specificity (True Negative Rate)
Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start1 Jul 2023
- Primary completionJul 2025 (estimated)
- Study completionJul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AI AgentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 2,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791499
- Sponsor's protocol ID
- AI-agent
- Status last verified
- Apr 2025
- First submitted
- 19 Jan 2025
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data
- ClinicalTrials.gov ID
- NCT06791499
- Acronym / sponsor ID
- AI-agent
- Conditions
- AI Agent
- Keywords
- AI agent
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- Jul 2025 (estimated)
- Study completion
- Jul 2025 (estimated)
- First posted
- 24 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.
Detailed description
This multi-center, retrospective clinical study is designed to evaluate the application and effectiveness of an AI agent in the medical decision-making process. The AI agent integrates and analyzes multimodal data, including electronic health records (EHR) and various imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds) to predict and diagnose a range of diseases. By leveraging the power of machine learning and data fusion techniques, the AI agent can identify patterns in large and complex datasets, offering insights that may not be immediately apparent through traditional diagnostic methods.The study will compare the AI agent's diagnostic accuracy and disease prediction capabilities with traditional diagnostic practices to assess its potential benefits in clinical settings. Key questions include whether the AI agent can assist in early diagnosis, predict disease progression, and support healthcare professionals in making personalized treatment decisions. Participants will not be required to undergo any additional interventions; they will only provide historical health data, including EHR and relevant imaging data, which will be analyzed by the AI agent. The AI system will then use this data to assist healthcare providers by offering predictions and diagnostic suggestions based on the analysis of the multimodal information. The ultimate goal is to determine whether this AI-driven approach can improve diagnostic accuracy, optimize treatment strategies, and enhance patient outcomes in clinical practice.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Disease Prediction Using EHR and Imaging Data | – | –This cohort consists of patients whose historical health data, including electronic health records (EHR) and multimodal imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds), will be analyzed by an AI agent. The AI system will assist in diagnosing and predicting diseases by processing and integrating these diverse data sources. The primary focus is to evaluate the ability of the AI agent to identify patterns and predict disease progression with high accuracy. Participants will not be required to take any additional actions beyond providing their medical history and imaging data. The aim is to assess how well the AI system can support clinical decision-making and improve diagnostic outcomes based on the provided data. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The participants for this study will be selected from multiple healthcare centers and hospitals that maintain comprehensive electronic health records (EHR) and multimodal imaging data. The study population will include patients who have a variety of diseases or health conditions, with data available for diagnosis and disease progression. Participants will have confirmed diagnoses based on clinical records or imaging data, including but not limited to conditions captured by X-rays, CT scans, MRIs, and ultrasounds. Both those with complex health conditions and those with more common illnesses will be included to evaluate the AI system's diagnostic and predictive capabilities across a broad spectrum of cases.Participants from these centers will provide historical health data, and there will be no active intervention beyond the use of their existing clinical and imaging data for training and testing the AI system.
Key inclusion criteria (4 listed)
- Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.
- Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.
- Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.
- Patients must provide consent for the use of their historical health data for research purposes.
Key exclusion criteria (2 listed)
- Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.
- Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Curve (AUC)
Time frame: 1 year
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
F1 Score
Time frame: 1 year
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
2 secondary outcomes
Sensitivity (True Positive Rate)
Time frame: 1 year
Specificity (True Negative Rate)
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of AI Agent, run by The Eye Hospital of Wenzhou Medical University at 6 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2025.